Driven by the domain knowledge that DTI happens in a sub-structural level, MolTrans first decomposes molecular sequence for proteins and drugs into sub-structures.
In particular, we propose a data-driven sequential pattern mining algorithm called Frequent Consecutive Sub-sequence Algorithm (\(\mathrm {FCS}\) ) to find recurring sub-sequences across drug and protein databases. Inspired by the invention of sub-word units in the natural language processing field [1]}, [2]}, \(\mathrm {FCS}\)  aims to generate a set of hierarchy of frequent sub-sequences for sequences. The algorithm is summarized in Algo. Using \(\mathrm {FCS}\)  algorithm, MolTrans converts input drug and target to a sequence of explicit sub-structures \(\mathbf {C}_\mathrm {d}\)  and \(\mathbf {C}_\mathrm {p}\)  respectively. The significance of \(\mathrm {FCS}\)  is two-folds:
